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Adapting Research to the Clinical Environment

Computer-aided Verbal Behavior Analysis

Angela Cathey, MA1, Gareth Holman3, PhD, Matt Villatte4, PhD, Robert Zettle1, PhD, Dominic Canare2, MSc, & Jeff Swails1, MA

Wichita State University (Clinical1 and Human Factors2),

OpenTeam3, Evidence-based Practice Institute4

Presentation for the Research in Clinical Practice SIG Funding Support: Shocker Innovation NSF i-Corps Grant

Patent # 62/307,226 ACBS WC14 Seattle, WA; June 18th, 2016

Angela Cathey Concept & Logistics

Gareth Holman Clinical Utility

Matt Villatte RFT Consult


Contextual Behavioral Scientific Innovation

Disclosures: Angela Cathey has received a small seed grant from the NSF for development, no other contributors have received any funding or benefit from their contributions

Progress Patent obtained (Cathey, 2016; Patent # 62/307,226)

Next Steps Clinician/researcher participation in development www.surveymonkey.com/r/CAVBA Phase 2 NSF i-Corps and SBIR/STTR Funding

✔

✔ NSF i-Corps Stage 1 funding

Let’s walk together towards a solution.

www.surveymonkey.com/r/CAVBA Twitter: @catalyst_reach

Standard assessment Contextually insensitive

Retrospective report bias

Mood/state dependent bias

Function?

www.surveymonkey.com/r/CAVBA

Assessments in practice…

Take away valuable client-therapist time

Clients are rarely adherent Don’t like them Don’t understand them


CBS consistent solutions have been proposed, including EMA

EMA allows for single-subject real-time data on client progress

But for the average clinician or researcher…

Not feasible


What else can we do?

Cloud computing Bandicoot – MIT Media

Biometrics


These solutions do not utilize our most powerful tools.

External Relating Verbal/Symbolic Relating


Teach computers functional analysis and a nearly all encompassing theory of human symbolic thought?


Signal Processing FA & RFT

When stimuli are related, the amount and way predict the

outcome

Patterns in language, external

environmental cues, and physiological

response


Simone M. She’s a 40 year-old, nurse with two children. She’s been married for six years and reports history of difficult relationships, depression, and anxiety. She also reports a long history of binge/purge behavior and cutting. www.surveymonkey.com/r/CAVBA

You’ve had a few sessions together and begun to work on Interpersonal mindfulness and mood regulation. She’s been fairly distressed but seems to find your work together helpful. However, she misses sessions frequently and its unclear why.


You’ve now had four sessions with her and she’s missed three. During this session she talks about fairly benign material and seems sad. She discusses difficult interactions with her loved ones. You respond with empathy, understanding, normalization. Unexpectedly, she becomes angry with you and walks out. What happened?


Let’s dive into the layers of signal and context

Client – Client’s environment

Relational environment within and outside session

Current environment vs. verbal symbolic environment (learning history)


How does this show up?

Content

Affect

Biometric

Changes in language that reliably precede or follow behavior of interest

Changes in affect that precede or follow behavior events

Changes in physiology related to environment

Mimetics, proxemics

Paraverbals (speech tone, rhythm, etc.)

Latent semantic content (metaphor, etc.)

Level and directedness of attention


It’s easy to get swept away in all the information…

But what if there were a single system that made sense of it?


A system to collect, analyze, and return data in useful form to clinicians and researchers


0

2

4

6

8

10

With Husband With Friend Alone At Work

Neg. Self

Urge to Binge

Urge to Cut

EMA Responses by Context

Treatment Week

0

50

100

150

200

Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7

Time with Husband

Time with Friend

Time Alone

Time at Work

Non

e to

Man

y H

ours

Simone’s EMA Data

What about in session behavior?

Simone Therapist

Other people just don’t see me. They don’t understand my needs. I’m glad I made it in today. I’m glad you made it in as well.

What’s been keeping you away so often? I noticed that you’ve missed a few sessions.

Oh. I just haven’t felt well… Okay, well let’s talk more about others not understanding your needs. What does that look like? What do you think it looks

like?

Sadness…

...Shame

...Anger

What could we have done for Simone?

If we’d have known . . . her work is punishing? . . . that when she doesn’t feel seen, she becomes fused and insensitive to the context?

What if you could prompt her with intervention support between sessions?

What if…

It can be…

…we could know how our clients function across life contexts?

…we could show our clients and insurance that we make real change?

…what if data from what really happens in sessions was available to researchers?


Assessing our impact

In-session behavior In-life behavior

1. Natural Language Processing

2. Biometrics

1. EMA 2. Natural Language Processing


…we need your support and feedback to navigate the seas ahead.

What would it need to have to be helpful to you, in practice?

… in research

Questions?

www.surveymonkey.com/r/CAVBA Twitter: @catalyst_reach

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References Cathey, A. (2016) Computer-aided Verbal Behavior Analysis; Patent

#62/307,226

Follette, W. C. & Bonow, J. T. (2009). The challenge of understanding

process in clinical behavior analysis: The case of Functional Analytic

Psychotherapy. The Behavior Analyst, 32(1),135-148.

Follette, W., Naugle, A. E., & Callaghan, G. M. (1996). A radical behavioral

understanding of the therapeutic relationship in effecting change.

Behavior Therapy, 27, 623-641.

Gaudiano, B. A. (2011). A review of Acceptance and Commitment Therapy

(ACT) and recommendations for continued scientific advancement.

The Scientific Review of Mental Health Practice, 8, 5-23.


References Holman, G. & Koerner, K. (2014). Single case designs in clinical

practice. A contemporary CBS perspective on why and how

to. Journal of Contextual Behavioral Science, 3(2), 138-147.

Hughes, S., Barnes-Holmes, D. & Vahey, N. (2012). Holding onto

our functional roots when exploring new intellectual islands:

A voyage through implicit cognition research. Journal of

Contextual Behavioral Science, 1, 17-38.

Roth, W., Vilardaga, R. Wolfe, N., Bricker, J., & McDonell, M.

(2014). Practical considerations in the design and

development of smartphone apps for behavior change.

Journal of Contextual Behavioral Science, 3, 269-272.

References Vilardaga, R. (2014). Technical, practical, and analytical innovations in

single case designs for contextual behavioral scientists. Journal of

Contextual Behavioral Science, 3, 136-137.

Vilardaga, R., Bricker, J. B., McDonell, M. G. (2014). The promise of

mobile technologies and single case designs for the study of

individuals in their natural environment. Journal of Contextual

Behavioral Science, 3, 148-153.

Vilardaga, R., Hayes, S. C., & Levin, M. (2009). Creating a strategy for

progress: A Contextual Behavioral Science approach. The Behavior

Analyst, 32, 105- 133.

References

Villatte, M., Villatte, J. L., & Hayes, S. C. (2016). Mastering the clinical

conversation. New York, NY: Guildford Press.

Wilson, K. G. (2001). Some notes on theoretical constructs: Types of

validation from a Contextual Behavioral Perspective. International

Journal of Psychology and Psychological Therapy, 1, 205-215.

Wilson, K. G., DuFrene, T., Sandoz, E., & Kellum, K. (2009). A clinician’s

guide to stimulus control. In K. G. Wilson and T. DuFrene (Eds.)

Mindfulness for two: An Acceptance and Commitment Therapy

approach to mindfulness in psychotherapy. (pp. 23-44) Oakland, CA:

New Harbinger.

References: Natural Language Processing Atkins & Styles (2016). Measuring the self and rules in what people say:

Exploring whether self-discrimination predicts long-term well being.

Journal of Contextual Behavioral Science, 5, 71-79.

Bird, S. Klein, E., & Loper, E. (2009). Natural Language Processing with

Python. O’Reilly: Sebastopol, CA.

Collins, S. E., Chawla, N., Hsu, S. U., Grow, J., Otto, J.M., & Marlatt, G. A.

(2009). Language-based measures of mindfulness: Initial validity and

clinical utility. Psychology of Addictive Behaviors, 23, 743-749.

He, Q., Veldkamp, B. P., de Vries, T. (2012). Screening for PTSD using verbal

features of self narratives: A text mining approach. Psychiatry Research,

198, 441 -447.

References: Natural Language Processing Pennebaker, J. W., Mehl, M. R., Neiderhoffer, K. G. (2003). Psychological aspects

of natural language use: Our words, ourselves. Annual Review of Psychology,

54, 547- 577.

Richardson, B. H., Taylor, P. J., Snook, B., Conchie, S. M., & Bennell, C.

(2014). Language style matching and police interrogation outcomes. Law

and Human Behavior, 38,1-10. (Reciprocal influence in languaging)

Short, J. C., Broberg, J. C., Cogliser, C. C. & Brigham K. H. (2010). Construct

validation using computer-aided text analysis (CATA): An illustration

using entrepreneurial orientation. Organizational Research Methods, 13,

320-347. (Traditional Psychometric Perspective on NLP Construct Validity).

References: Modeling Snijders, T. A. B. (2005). Power and sample size in multilevel linear

models, In B.S. Everitt and D.C. Howell (Eds.), Encyclopedia of

Statistics in Behavioral Science, 3, (pp. 1570-1573) Chicester, UK: Wiley.

Stone, A. A., Shiffman, S., Atienza, A. A. & Nebling, L. (2007). The science

of real-time data capture: Self-reports in Health Research. New York,

NY: Oxford Press.

Targio Hashem, I. A., Yaqoob, I., Badrul Anuar, N., Mokhtar, S., Gani, A.,

Ullahkan, S. (2015). The rise of “big data” on cloud computing:

Review and open research issues. Information Systems, 47, 98-115.

Walls, T. & Schafer, J. L. (2006). Models for intensive longitudinal data.

New York, NY: Oxford Press.

Clinician Analytics The Clinician Analytics Dashboard will contain cross client and process data. Insights: How good am I at moving flexibility? How good am I at moving clients with ____ class of behavior? Database: Cross theoretical search of techniques that move a process of interest.


Relational Frame

Theory (RFT)

Ecological Momentary Assessment

(EMA)

Natural Language Processing

(NLP)

Data Science: Machine Learning,

analytics, etc.

Application

Everything post-verbal

learning

Real-time data

collection and

intervention

Processing of language as it

relates to real-world outcomes

Preferred for anything that involves high

velocity, variability,

and volume data

Fields using

CBS

CBS + many, many others

Business English (Lit.)

Medicine

Business Medicine

Evidence

20+ years

20+ years

20+ years

20+ years

Reason for lag

in use

Basic to applied gap

Skills needed to design

system & do analyses

Expensive

until recently

Expensive

until recently


EMA items: Vilardaga, McDonell, Leickley, & Ries (2014)

CAVBA Programming/Data Flow Architecture


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